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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

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Author(s):
Faria, Fabio A. [1] ; Pedronette, Daniel C. G. [2] ; dos Santos, Jefersson A. [3] ; Rocha, Anderson [1] ; Torres, Ricardo da S. [1]
Total Authors: 5
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Sao Paulo - Brazil
[2] State Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, BR-13506900 Sao Paulo - Brazil
[3] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 7, n. 4, p. 1103-1115, APR 2014.
Web of Science Citations: 9
Abstract

In the past few years, segmentation and classification techniques have become a cornerstone of many successful remote sensing algorithms aiming at delineating geographic target objects. One common strategy relies on using multiple complex features to guide the delineation process with the objective of gathering complementary information for improving classification results. However, a persistent problem in this approach is how to combine different and noncorrelated feature descriptors automatically. In this regard, one solution is to combine them through multiple classifier systems (MCSs) in which the diversity of simple/non-complex classifiers is an essential issue in the definition of appropriate strategies for classifier fusion. In this paper, we propose a novel strategy for selecting classifiers (whereby a classifier is taken as a pair of learning method plus image descriptor) to be combined in MCS. In the proposed solution, diversity measures are used to assess the degree of agreement/disagreement between pairs of classifiers and ranked lists are created to sort them according to their diversity score. Thereafter, the classifiers are also sorted according to their performance through different evaluation measures (e. g., kappa and tau indices). In the end, a rank aggregation method is proposed to select the most suitable classifiers based on both the diversity and the effectiveness performance of classifiers. The proposed fusion framework has targeted at coffee crop classification and urban recognition but it is general enough to be used in a variety of other pattern recognition problems. Experimental results demonstrate that the novel strategy yields good results when compared to several baselines while using fewer classifiers and being much more efficient. (AU)

FAPESP's process: 10/14910-0 - Evidence-Fusion Methods for Multimedia Retrieval and Classification
Grantee:Fabio Augusto Faria
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences
Grantee:Anderson de Rezende Rocha
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 12/18768-0 - Multiscale Classification By Using Optimum Path-Forest
Grantee:Jefersson Alex dos Santos
Support type: Scholarships in Brazil - Post-Doctorate